Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D

2024-09-20

IEEE Access

AI Summary

Existing structure tensor-based scale-space methods suffer from parameter sensitivity, inaccurate feature-scale estimation, and edge-biased response centers. To address these issues, this paper proposes a feature-size-driven scale-space construction framework. Our method introduces three key innovations: (1) a novel feature-centered derivative filtering mechanism that adaptively adjusts the width of differential operators according to local feature size; (2) an annular filter design enabling precise migration of response centers from edges to true feature centers; and (3) a structure-measure-guided scale-map backward correction algorithm, achieving the first parameter-free, robust 2D/3D feature-scale representation. Experiments demonstrate substantial improvements in scale estimation accuracy and stability. The framework enables out-of-the-box extraction of diverse structural parameters—e.g., orientation, anisotropy, and curvature—without manual tuning, consistently outperforming conventional structure tensor approaches and state-of-the-art scale-space methods.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Other Foundations of Computer Vision
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user’s choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2D and 3D. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.

Problem

• Feature Centralized First-order Structure Tensor
• Parameter Sensitivity
• Scale Space Representation

Innovation

• Feature-Centric Scale Space
• Adaptive Derivative Filter
• Automated Parameter Setting